




'''simple docstring'''


from __future__ import annotations


def     SCREAMING_SNAKE_CASE							(   a_						:				str					,       a_						:				list[str] | None = None					,       a_						:				dict[str, float] | None = None					,       a_						:				bool = False					,       ):
  __a								=       cipher_alphabet or [chr(a_      ) for i in range(97					,       123      )]

  # If the argument is None or the user provided an empty dictionary
  if not frequencies_dict:
    # Frequencies of letters in the english language (how much they show up)
    __a								=       {
        'a': 0.0_84_97,
        'b': 0.0_14_92,
        'c': 0.0_22_02,
        'd': 0.0_42_53,
        'e': 0.1_11_62,
        'f': 0.0_22_28,
        'g': 0.0_20_15,
        'h': 0.0_60_94,
        'i': 0.0_75_46,
        'j': 0.0_01_53,
        'k': 0.0_12_92,
        'l': 0.0_40_25,
        'm': 0.0_24_06,
        'n': 0.0_67_49,
        'o': 0.0_75_07,
        'p': 0.0_19_29,
        'q': 0.0_00_95,
        'r': 0.0_75_87,
        's': 0.0_63_27,
        't': 0.0_93_56,
        'u': 0.0_27_58,
        'v': 0.0_09_78,
        'w': 0.0_25_60,
        'x': 0.0_01_50,
        'y': 0.0_19_94,
        'z': 0.0_00_77,
    }
  else:
    # Custom frequencies dictionary
    __a								=       frequencies_dict

  if not case_sensitive:
    __a								=       ciphertext.lower()

  # Chi squared statistic values
  __a								=       {}

  # cycle through all of the shifts
  for shift in range(len(a_      )      ):
    __a								=       ''

    # decrypt the message with the shift
    for letter in ciphertext:
      try:
        # Try to index the letter in the alphabet
        __a								=       (alphabet_letters.index(letter.lower()      ) - shift) % len(
            a_      )
        decrypted_with_shift += (
            alphabet_letters[new_key].upper()
            if case_sensitive and letter.isupper()
            else alphabet_letters[new_key]
        )
      except ValueError:
        # Append the character if it isn't in the alphabet
        decrypted_with_shift += letter

    __a								=       0.0

    # Loop through each letter in the decoded message with the shift
    for letter in decrypted_with_shift:
      if case_sensitive:
        __a								=       letter.lower()
        if letter in frequencies:
          # Get the amount of times the letter occurs in the message
          __a								=       decrypted_with_shift.lower().count(a_      )

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          __a								=       frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          __a								=       ((occurrences - expected) ** 2) / expected

          # Add the margin of error to the total chi squared statistic
          chi_squared_statistic += chi_letter_value
      else:
        if letter.lower() in frequencies:
          # Get the amount of times the letter occurs in the message
          __a								=       decrypted_with_shift.count(a_      )

          # Get the excepcted amount of times the letter should appear based
          # on letter frequencies
          __a								=       frequencies[letter] * occurrences

          # Complete the chi squared statistic formula
          __a								=       ((occurrences - expected) ** 2) / expected

          # Add the margin of error to the total chi squared statistic
          chi_squared_statistic += chi_letter_value

        # Add the data to the chi_squared_statistic_values dictionary
    __a								=       (
        chi_squared_statistic,
        decrypted_with_shift,
    )

  # Get the most likely cipher by finding the cipher with the smallest chi squared
  # statistic
  def chi_squared_statistic_values_sorting_key(a_						:				int      ) -> tuple[float, str]:
    return chi_squared_statistic_values[key]

  __a								=       min(
      a_					,       key=a_					,       )

  # Get all the data from the most likely cipher (key, decoded message)
  (
      (
      __a
  )						,							(
      __a
  )						,							
  )								=       chi_squared_statistic_values[most_likely_cipher]

  # Return the data on the most likely shift
  return (
      most_likely_cipher,
      most_likely_cipher_chi_squared_value,
      decoded_most_likely_cipher,
  )









'''simple docstring'''


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
  from ... import FeatureExtractionMixin, TensorType

UpperCAmelCase_					=   logging.get_logger(__name__)

UpperCAmelCase_					=   {
    "openai/imagegpt-small": "",
    "openai/imagegpt-medium": "",
    "openai/imagegpt-large": "",
}





class  __lowercase    (	__magic_name__			):
   _a    						=   """imagegpt"""
   _a    						=   ["""past_key_values"""]
   _a    						=   {
       """hidden_size""": """n_embd""",
       """max_position_embeddings""": """n_positions""",
       """num_attention_heads""": """n_head""",
       """num_hidden_layers""": """n_layer""",
   }







   def __init__(						self   ,				UpperCamelCase=512 + 1   ,				UpperCamelCase=32 * 32   ,				UpperCamelCase=512   ,				UpperCamelCase=24   ,				UpperCamelCase=8   ,				UpperCamelCase=None   ,				UpperCamelCase="quick_gelu"   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.1   ,				UpperCamelCase=0.1   ,				UpperCamelCase=1e-5   ,				UpperCamelCase=0.02   ,				UpperCamelCase=True   ,				UpperCamelCase=True   ,				UpperCamelCase=False   ,				UpperCamelCase=False   ,				UpperCamelCase=False   ,				**UpperCamelCase   ,				)					->     int:
     __a								=       vocab_size
     __a								=       n_positions
     __a								=       n_embd
     __a								=       n_layer
     __a								=       n_head
     __a								=       n_inner
     __a								=       activation_function
     __a								=       resid_pdrop
     __a								=       embd_pdrop
     __a								=       attn_pdrop
     __a								=       layer_norm_epsilon
     __a								=       initializer_range
     __a								=       scale_attn_weights
     __a								=       use_cache
     __a								=       scale_attn_by_inverse_layer_idx
     __a								=       reorder_and_upcast_attn
     __a								=       tie_word_embeddings

     super().__init__(tie_word_embeddings=UpperCamelCase   ,				**UpperCamelCase   )





class  __lowercase    (	__magic_name__			):







   @property
   def 		UpperCamelCase__      (						self   )					->     Mapping[str, Mapping[int, str]]:
     return OrderedDict(
         [
             ('input_ids', {0: 'batch', 1: 'sequence'}),
         ]   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase = 1   ,				UpperCamelCase = -1   ,				UpperCamelCase = False   ,				UpperCamelCase = None   ,				UpperCamelCase = 3   ,				UpperCamelCase = 32   ,				UpperCamelCase = 32   ,				)					->     Mapping[str, Any]:
     __a								=       self._generate_dummy_images(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )
     __a								=       dict(preprocessor(images=UpperCamelCase   ,				return_tensors=UpperCamelCase   )   )

     return inputs



